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• Evaluating artificial intelligence for medical imaging: a primer for clinicians.
  Artificial intelligence has the potential to transform medical imaging. The effective integration of artificial intelligence into clinical practice requires a robust understanding of its capabilities and limitations. This paper begins with an overview of key c…
  pubmed Keni S 2024 Jul 30 置信度 0.82
• Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics.
  This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that inte…
  pubmed Castro C, Leiva V, Garrido D, Huerta M 等 2024 Oct 置信度 0.82
• Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.
  Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search…
  pubmed von Gerich H, Chomutare T, Peltonen LM 2024 Jul 24 置信度 0.82
• Recent methodological advances in federated learning for healthcare.
  For healthcare datasets, it is often impossible to combine data samples from multiple sites due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of powerful machine learning algorithms without requiring the pooling of …
  pubmed Zhang F, Kreuter D, Chen Y, Dittmer S 等 2024 Jun 14 置信度 0.82
• Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.
  Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing …
  pubmed Jeon K, Park WY, Kahn CE Jr, Nagy P 等 2025 Jan 1 置信度 0.82
• Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects.
  Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource…
  pubmed Alsadie D 2024 置信度 0.82
• Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.
  Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information re…
  pubmed Vistorte AOR, Deroncele-Acosta A, Ayala JLM, Barrasa A 等 2024 置信度 0.82
• Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.
  Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancemen…
  pubmed Gangwal A, Ansari A, Ahmad I, Azad AK 等 2024 Sep 置信度 0.82
• Artificial intelligence innovations in neurosurgical oncology: a narrative review.
  Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.
  pubmed Baker CR, Pease M, Sexton DP, Abumoussa A 等 2024 Sep 置信度 0.82
• Evaluation and utilisation of privacy enhancing technologies-A data spaces perspective.
  Data sharing has facilitated the digitisation of society. We can access our bank accounts or make an appointment with our doctor anytime and anywhere. To achieve this, we have to share certain information, whether personal, professional, etc. This may seem lik…
  pubmed Auñón JM, Hurtado-Ramírez D, Porras-Díaz L, Irigoyen-Peña B 等 2024 Aug 置信度 0.82
• Artificial Intelligence and Multiple Sclerosis.
  In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learn…
  pubmed Amin M, Martínez-Heras E, Ontaneda D, Prados Carrasco F 2024 Aug 置信度 0.82
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.
  Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this sur…
  pubmed Huang W, Ye M, Shi Z, Wan G 等 2024 Dec 置信度 0.82
• Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images.
  The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of…
  pubmed Ahmed R, Maddikunta PKR, Gadekallu TR, Alshammari NK 等 2024 置信度 0.82
• Learning from Global Models: A Comparative Analysis of Health Data Governance Frameworks and Their Implications for U.S. Health Information Policy and System Design
  Health data governance has become a critical component of modern healthcare systems due to increasing digitization, large-scale data sharing, and the growing importance of data-driven research and innovation. This review identifies key governance models and ex…
  datacite Omonye Jones Silas, Solomon Doe Adjaottor 2026 置信度 0.66
• Learning from Global Models: A Comparative Analysis of Health Data Governance Frameworks and Their Implications for U.S. Health Information Policy and System Design
  Health data governance has become a critical component of modern healthcare systems due to increasing digitization, large-scale data sharing, and the growing importance of data-driven research and innovation. This review identifies key governance models and ex…
  datacite Omonye Jones Silas, Solomon Doe Adjaottor 2026 置信度 0.66
• Supplementary Material for: Federated Learning for Ophthalmic Imaging: A Systematic Review and Quantitative Synthesis
  datacite Qi, Luyuan 2026 置信度 0.66
• Supplementary Material for: Federated Learning for Ophthalmic Imaging: A Systematic Review and Quantitative Synthesis
  datacite Qi, Luyuan 2026 置信度 0.66
• Deep Learning Strategies for Predicting Drug–Target Interactions: Advances, Challenges, and Future Perspectives
  Drug–target interaction (DTI) prediction lies at the heart of modern drug discovery, determining whether a candidate small molecule will bind to and modulate a biological macromolecule of therapeutic relevance. Traditional experimental high-throughput screenin…
  datacite Avinash Bajpai, Sachin Sharma, Birender Singh, KM Nisha 2026 置信度 0.66 Drug–Target Interaction; Deep Learning; Graph Neural Networks; Molecular Representation; Binding Affinity; Drug Discovery; Transformer; Alphafold
• Deep Learning Strategies for Predicting Drug–Target Interactions: Advances, Challenges, and Future Perspectives
  Drug–target interaction (DTI) prediction lies at the heart of modern drug discovery, determining whether a candidate small molecule will bind to and modulate a biological macromolecule of therapeutic relevance. Traditional experimental high-throughput screenin…
  datacite Avinash Bajpai, Sachin Sharma, Birender Singh, KM Nisha 2026 置信度 0.66 Drug–Target Interaction; Deep Learning; Graph Neural Networks; Molecular Representation; Binding Affinity; Drug Discovery; Transformer; Alphafold
• The Intersection of AI and Blockchain for Enhanced Financial Due Diligence
  Financial due diligence (FDD) plays a crucial role in corporate finance and mergers and acquisitions, because it is traditionally hampered by inefficiencies, human mistakes, and fractured verification mechanisms. The review examines how Artificial Intelligence…
  datacite Deborah Akuele Apaflo, Ifeyinwa Perpetual Nwinyi, Barnabas Anim, William Kweku Afresi Buabin 等 2026 置信度 0.66
• The Intersection of AI and Blockchain for Enhanced Financial Due Diligence
  Financial due diligence (FDD) plays a crucial role in corporate finance and mergers and acquisitions, because it is traditionally hampered by inefficiencies, human mistakes, and fractured verification mechanisms. The review examines how Artificial Intelligence…
  datacite Deborah Akuele Apaflo, Ifeyinwa Perpetual Nwinyi, Barnabas Anim, William Kweku Afresi Buabin 等 2026 置信度 0.66
• Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey
  The evolution toward sixth-generation (6G) networks is transforming the radio access network (RAN) into a programmable and intelligent control platform that must continuously adapt to heterogeneous services, dynamic environments, and competing performance obje…
  datacite Lu, Jie, Yan, Peihao, Wang, Qijun, Lin, Ruxin 等 2026 置信度 0.66 Networking and Internet Architecture (cs.NI)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Coordination Topology as Failure Geometry: How Network Structure, Protocol Design, and Consensus Dynamics Jointly Shape Multi-Agent System Reliability
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. Multi-agent AI systems are increasingly deployed in production settings where reliability is assum…
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• Feature-Oriented Regulation in Federated Multimodal Models Under Non-IID Data Distributions
  This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the impact of feature-oriented regulation methods like \$Psi\$-Net on the inference efficiency of federated multimodal models under non-IID data di…
  datacite Assignee Research 2026 置信度 0.66 impactfeature-orientedregulationmethodslike
• Feature-Oriented Regulation in Federated Multimodal Models Under Non-IID Data Distributions
  This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the impact of feature-oriented regulation methods like \$Psi\$-Net on the inference efficiency of federated multimodal models under non-IID data di…
  datacite Assignee Research 2026 置信度 0.66 impactfeature-orientedregulationmethodslike
• Intelligent Predictive Architectures for Autonomous Self-Healing in Cloud Computing: A Comprehensive Survey
  Neural network-enabled self-healing is becoming a very exciting approach to making cloud computing infrastructures more reliable, available, and efficient. This survey paper gives a detailed review of neural network-based predictive models and how they are com…
  datacite Mr. Nitesh Gupta, Dr. Nandita Bangera 2026 置信度 0.66 Neural Predictive IntelligenceSelf-Healing ArchitecturesDeep Learning for Cloud SystemsMulti-Agent Reinforcement LearningFederated Self-Healing Frameworks
• Intelligent Predictive Architectures for Autonomous Self-Healing in Cloud Computing: A Comprehensive Survey
  Neural network-enabled self-healing is becoming a very exciting approach to making cloud computing infrastructures more reliable, available, and efficient. This survey paper gives a detailed review of neural network-based predictive models and how they are com…
  datacite Mr. Nitesh Gupta, Dr. Nandita Bangera 2026 置信度 0.66 Neural Predictive IntelligenceSelf-Healing ArchitecturesDeep Learning for Cloud SystemsMulti-Agent Reinforcement LearningFederated Self-Healing Frameworks
• Artificial Intelligence, IoV, And Security In Modern Intelligent Systems: A Holistic Study
  Recent breakthroughs in Artificial Intelligence (AI), Internet of Vehicles (IoV), Brain–Computer Interfaces (BCI), blockchain security, autonomous driving, and speech processing are reshaping intelligent communication and automation systems. This review synthe…
  datacite Mustaq Kunnur 2026 置信度 0.66
• Artificial Intelligence, IoV, And Security In Modern Intelligent Systems: A Holistic Study
  Recent breakthroughs in Artificial Intelligence (AI), Internet of Vehicles (IoV), Brain–Computer Interfaces (BCI), blockchain security, autonomous driving, and speech processing are reshaping intelligent communication and automation systems. This review synthe…
  datacite Mustaq Kunnur 2026 置信度 0.66
• Machine Learning Models For Predictive Cybersecurity Defense
  Machine learning has emerged as a transformative force in cybersecurity, enabling predictive defence mechanisms that move beyond traditional reactive strategies. This review explores the evolution, methodologies, and applications of machine learning models in …
  datacite Manoj Tiwari 2020 置信度 0.66
• Machine Learning Models For Predictive Cybersecurity Defense
  Machine learning has emerged as a transformative force in cybersecurity, enabling predictive defence mechanisms that move beyond traditional reactive strategies. This review explores the evolution, methodologies, and applications of machine learning models in …
  datacite Manoj Tiwari 2020 置信度 0.66
• AI-Assisted Clinical Trial Recruitment for Devices
  Clinical trial recruitment is the most frequent cause of device trial delay and the most preventable. More than 80% ofdevice trials fail to meet original enrolment targets on schedule, extending development timelines by a median of 8-14months and generating co…
  datacite Marta Mulle, Nina Klein, Andreas Bianchi 2025 置信度 0.66 clinical trial recruitment; AI recruitment; NLP screening; device trials; patient matching; federated learning; digital outreach; AADTRF; Recruitment Effectiveness Score; screen failure; trial enrolment
• AI-Assisted Clinical Trial Recruitment for Devices
  Clinical trial recruitment is the most frequent cause of device trial delay and the most preventable. More than 80% ofdevice trials fail to meet original enrolment targets on schedule, extending development timelines by a median of 8-14months and generating co…
  datacite Marta Mulle, Nina Klein, Andreas Bianchi 2025 置信度 0.66 clinical trial recruitment; AI recruitment; NLP screening; device trials; patient matching; federated learning; digital outreach; AADTRF; Recruitment Effectiveness Score; screen failure; trial enrolment
• Artificial intelligence-assisted early warning and continuous monitoring system for prevention of cardiac emergencies: A comprehensive review
  Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, c…
  datacite G. Elango, P. Sumithra, Salomeen Rani. S 2026 置信度 0.66
• Artificial intelligence-assisted early warning and continuous monitoring system for prevention of cardiac emergencies: A comprehensive review
  Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, c…
  datacite G. Elango, P. Sumithra, Salomeen Rani. S 2026 置信度 0.66
• AI-Based Image Analysis in Diagnostic Devices
  AI-based image analysis has emerged as the leading application of artificial intelligence in diagnostic medical devices,with over 520 FDA-cleared AI/ML-enabled devices by 2023 -- the majority addressing radiology, pathology,ophthalmology, and dermatology image…
  datacite Helena Popescu, Andreas Lindberg, Andreas Moreau 2024 置信度 0.66 AI image analysis; diagnostic device; convolutional neural network; federated learning; radiology; pathology; retinal imaging; autonomous reporting; SaMD; workflow integration
• AI-Based Image Analysis in Diagnostic Devices
  AI-based image analysis has emerged as the leading application of artificial intelligence in diagnostic medical devices,with over 520 FDA-cleared AI/ML-enabled devices by 2023 -- the majority addressing radiology, pathology,ophthalmology, and dermatology image…
  datacite Helena Popescu, Andreas Lindberg, Andreas Moreau 2024 置信度 0.66 AI image analysis; diagnostic device; convolutional neural network; federated learning; radiology; pathology; retinal imaging; autonomous reporting; SaMD; workflow integration
• Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions
  Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns. Federated learning (FL…
  datacite Yang, Qinglin, Qiu, Chen, Zhang, Hongyuan, Li, Pengdeng 等 2026 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• Spectroscopically Constrained Quantum Machine Learning for Complex Systems: Applying the Maxwell-Scretching Framework, the Scretching Quantum Chain, and the Scretching-Schrodinger Equation to QML
  This paper applies the Maxwell–Scretching Framework, the Scretching Quantum Chain (SQC), and the Scretching–Schrödinger Equation (SSE) to the quantum machine-learning review by Singh, Bhatia, Saggi, Sajjan, and Kais, Quantum Machine Learning for Complex System…
  datacite Scretching, Daniel 2026 置信度 0.66
• Spectroscopically Constrained Quantum Machine Learning for Complex Systems: Applying the Maxwell-Scretching Framework, the Scretching Quantum Chain, and the Scretching-Schrodinger Equation to QML
  This paper applies the Maxwell–Scretching Framework, the Scretching Quantum Chain (SQC), and the Scretching–Schrödinger Equation (SSE) to the quantum machine-learning review by Singh, Bhatia, Saggi, Sajjan, and Kais, Quantum Machine Learning for Complex System…
  datacite Scretching, Daniel 2026 置信度 0.66
• The Convergence of Federated Learning, Knowledge Graphs, and Large Language Models for Language Learning: A Scoping Review
  Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning enable highly personalized learning, yet raise significant challenges related to pedagogical grounding, data privacy, and instructional validity. Although Knowledge Graphs (KGs) an…
  openalex Michael Kenteris, Konstantinos Kotis 2026-03-09 置信度 0.72 Computer scienceDisconnectionKnowledge graphPillarGraph
• Machine Learning for Sustainable Shipping: Predicting Vessel CO2 Emissions Using Random Forest Models
  Predicting fuel consumption in the shipping industry is a critical task that supports optimized operations, driving both economic and environmental benefits as global demand for shipping continues to grow. However, accurately forecasting Carbon Dioxide (CO2) e…
  crossref Carol Anne Hargreaves, Briana Wan Nee Toh 2025-01-23T12:59:30Z 置信度 0.70
• Network Intrusion Detection Empowered with Federated Machine Learning
  Abstract Security and privacy are greatly enhanced by intrusion detection systems. Now, Machine Learning (ML) and Deep Learning (DL) with Intrusion Detection Systems (IDS) have seen great success due to their high levels of classification accuracy. Nevertheles…
  crossref Muhammad Umar Nasir, Shahid Mehmood, Muhammad Adnan Khan, Muhammad Zubair 等 2023-09-19T17:31:17Z 置信度 0.70
• PRIVACY-CONSCIOUS FEDERATED MULTI-MODAL LEARNING WITH JURISDICTIONAL CONSTRAINTS
  The rapid-fire proliferation of Internet of effects ( IoT) bias has introduced significant security challenges, particularly in large- scale and miscellaneous IoT networks. These systems are decreasingly susceptible to different cyber-attacks due to the decent…
  crossref Pooja Upadhyay 2026-02-03T19:13:18Z 置信度 0.70
• Research on Cross-Platform Digital Advertising User Behavior Analysis Framework Based on Federated Learning
  This information is released over the digital movement based on behavioral courses for user assessment. The framework is important to challenge the privacy-keeping the data division and manipulation throughout the platform announced. The network network archit…
  openalex Kai Zhang, Suchuan Xing, Yizhe Chen, Y H Chen 2024-07-13 置信度 0.72 Computer scienceCross-platformWorld Wide WebOperating system
• Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review
  In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine (ML) learning models in a distributed fashion without re…
  crossref Asad Ali, Inaam Ilahi, Adnan Qayyum, Ihab Mohammed 等 2021-07-14T03:46:43Z 置信度 0.70
• Enhancing Federated Learning for Privacy Preserving Intrusion Detection in IoT networks
  crossref Mohammed BENKADDOUR 2025-10-03T07:09:58Z 置信度 0.70
• Advancements and Challenges in Federated Learning for Privacy-Preserving Smart Healthcare: A Review
  crossref Habiba Akter Rimi, Md. Asaduzzaman, Md. Johir Uddin Bhuiyan, Hashibul Ahsan Shoaib 等 2025-10-31T19:42:35Z 置信度 0.70
• Proposed Methodology for Disaster Classification Using Computer Vision and Federated Learning
  Abstract Classification of disasters is crucial for effectivedisaster management and response. This paper proposes amethodology that combines computer vision techniques andfederated learning to improve the classification accuracy ofdisasters while addressing t…
  crossref Jash Shah, Divya Patel, Jinish Shah, Saurav Shah 等 2023-07-17T05:58:45Z 置信度 0.70
• Online Federated Learning over decentralized networks
  crossref Chi Zhang 2019-09-11T03:35:35Z 置信度 0.70
• Quantized Federated Learning
  crossref 2022-06-16T00:05:40Z 置信度 0.70
• Deep Learning and Federated Learning for Screening COVID-19: A Review
  Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of res…
  crossref M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder, Joarder Kamruzzaman 2023-09-01T09:24:53Z 置信度 0.70
• Opportunistic Networking and the Future of Context-Aware Recommendations
  In environments where internet connectivity is limited or disrupted, ensuring continuous access to personalized information presents significant challenges. This chapter introduces a distributed collaborative recommender system designed for opportunistic netwo…
  crossref Lucas Nunes Barbosa 2025-01-09T13:11:49Z 置信度 0.70
• Mechanism and Recent Trends of Federated Learning as a Privacy Technology
  crossref Takenobu SEITO 2022-12-31T22:16:03Z 置信度 0.70
• A Review on Hierarchical Federated Learning: Architectures and Learning Algorithms
  crossref Amani Chachoua, Abdelhamid Malki, Samir Ouchani 2026-01-19T20:52:38Z 置信度 0.70
• An Empirical Review of Federated Learning Methods for Client-Side Security and Communication Efficiency
  crossref Narendra Babu Pamul, Ajoy Kumar Khan, ARINDAM SARKAR 2025-03-26T03:31:22Z 置信度 0.70
• A Webshell Detection Method Based on Multi-Feature Fusion and Federated Learning
  Abstract Webshell attacks have become a prevalent strategy in the arsenal of network intruders, enabling them to seize a measure of control over web servers and execute malicious operations. These incursions are particularly insidious due to their covert natur…
  crossref Qing-peng ZENG, Jiang-li CHAI, Jian-sheng WU 2025-01-06T11:22:32Z 置信度 0.70
• SVFLDetector: A Decentralized Client Detection Method for Byzantine Problem in Vertical Federated Learning
  Abstract In recent years, with the deepening of cross-industry cooperation, vertical federated learning with multiple overlapping samples and fewer overlapping features has attracted extensive attention. Unlike horizontal federated learning, the heterogeneity …
  crossref Jiuyun Xu, Yinyue Jiang, Hanfei Fan, Qiqi Wang 2023-06-05T03:31:13Z 置信度 0.70
• Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review
  crossref Mansi Gupta, Mohit Kumar, Renu Dhir 2024-10-03T16:17:19Z 置信度 0.70
• PerFedNILM: A Practical Personalized Federated Learning-based non-Intrusive Load Monitoring
  Abstract Non-Intrusive Load Monitoring (NILM) is a valuable technique for breaking down overall power consumption into the energy usage of individual appliances. Understanding power usage patterns through NILM plays an important role in reducing energy costs a…
  crossref Zibin Pan, Haosheng Wang, Chi Li, Haijin Wang 等 2023-10-03T08:59:16Z 置信度 0.70
• Blockchain-Enabled Federated Learning Systems with Explainable AI: A Review
  crossref Nayan Potdukhe, Palash Gourshettiwar, Sujal Zade, Atharva Waghale 2025-04-25T17:38:13Z 置信度 0.70
• Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection
  Abstract The uses of Machine Learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated using data samples originating from the same organisational network. However, it has been very challenging …
  crossref Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, Marius Portmann 2022-05-10T14:45:28Z 置信度 0.70
• Federated Learning and Differential Privacy for Medical Image Analysis
  Abstract The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly avail…
  europepmc Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell, Graham W. Taylor 等 2021 置信度 0.80
• On Demand Deployment of Edge Cloud Infrastructures for Federated Learning
  Abstract Federated learning on the edge allows the use of more powerful servers and more complex training models. This paper presents the deployment of a real federated learning framework on top of a real geo-distributed edge computing infrastructure, based on…
  crossref Eduardo Huedo, Rafael Moreno-Vozmediano, Rubén S. Montero, Ignacio M. Llorente 2022-12-13T19:17:22Z 置信度 0.70
• PDTV-FEL: Privacy-preserving and Dual Traceable Verification Federated Edge Learning
  Abstract Federated edge learning (FEL) emerges as a novel distributed learning paradigm where multiple clients can jointly train a global model without collecting raw data. However, since adversaries can infer sensitive information from the global model and lo…
  crossref Pan Zhang, Lei Xu, Chungen Xu, Lin Mei 等 2025-03-31T08:03:23Z 置信度 0.70
• Blockchain-Mediated Federated Learning for Trustworthy Health Care AI: Systematic Review (Preprint)
  BACKGROUND Federated learning enables collaborative model training across health care institutions while preserving data locality, but it does not by itself resolve challenges related to trust, governance, auditability, and adversarial robustness. Blockchain h…
  crossref Rui Botelho, Goreti Marreiros, Luís Conceição 2026-06-29T08:35:07Z 置信度 0.70
• Introductory Chapter: Recent Trends and Progress in Support Vector Machines
  crossref Robertas Damaševičius 2025-04-02T13:31:30Z 置信度 0.70
• Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insights
  crossref Praveer Dubey, Mohit Kumar 2024-12-06T08:09:38Z 置信度 0.70
• Federated Learning for Privacy-Preserving Threat Detection in Massive IOT Ecosystems: A Systematic Literature Review
  Federated learning (FL) has emerged as a principal architecture for privacy-preserving intrusion detection in Internet of Things (IOT) environments, motivated by the impossibility of transmitting raw device traffic to a centralised server at scale. A rapidly g…
  datacite Peter Wonah Odey, Habibu Danjuma, Muhammad Salma Abidi 2026 置信度 0.66
• Federated Learning for Privacy-Preserving Threat Detection in Massive IOT Ecosystems: A Systematic Literature Review
  Federated learning (FL) has emerged as a principal architecture for privacy-preserving intrusion detection in Internet of Things (IOT) environments, motivated by the impossibility of transmitting raw device traffic to a centralised server at scale. A rapidly g…
  datacite Peter Wonah Odey, Habibu Danjuma, Muhammad Salma Abidi 2026 置信度 0.66
• TyMill/FedGridSim: FedGridSim v1.1.0
  FedGridSim v1.1.0 Release summary FedGridSim v1.1.0 is a research-oriented release of the FedGridSim framework for reproducible experiments on federated learning, physics-based smart-grid simulation, and cyber-physical resilience assessment. This release conso…
  datacite Tymoteusz Miller 2026 置信度 0.66
• TyMill/FedGridSim: FedGridSim v1.1.0
  FedGridSim v1.1.0 Release summary FedGridSim v1.1.0 is a research-oriented release of the FedGridSim framework for reproducible experiments on federated learning, physics-based smart-grid simulation, and cyber-physical resilience assessment. This release conso…
  datacite Tymoteusz Miller 2026 置信度 0.66
• MaxModShift: Model Privacy via Designed Shifts
  Model learning by an eavesdropper is treated as an estimation problem in a federated environment. The Fisher Information Matrix for the eavesdropper's estimation problem is driven to singularity through a signaling design; this ensures that the eavesdropper ca…
  datacite Kherani, Nomaan A., Mitra, Urbashi 2026 置信度 0.66 Machine Learning (cs.LG)Information Theory (cs.IT)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
  In a federated learning setup for GANs, several adversarial attacks are possible. One such attack is label flipping, in which malicious clients deliberately alter label information during local training in order to manipulate the global generator. The objectiv…
  datacite Shah, Panav, Ghosh, Avishek 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• WAA-A1/privacy-preserving-federated-ids: Updated Thesis Implementation – Version 2.0
  Updated implementation and experimental results for the 2026 Thesis. This release contains the updated federated intrusion detection implementation, experimental CSV results, generated figures, and documentation. The updated version includes evaluations of pri…
  datacite WAA-A1 2026 置信度 0.66
• AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies
  AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies Version 2.0 - Expanded Same-Day Edition Author: Dallas Courchene Date: August 7, 2026 Claim range: N51-N100 This expanded same-day edition supersedes the initial Aug…
  datacite Courchene, Dallas 2026 置信度 0.66
• AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies
  AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies Version 2.0 - Expanded Same-Day Edition Author: Dallas Courchene Date: August 7, 2026 Claim range: N51-N100 This expanded same-day edition supersedes the initial Aug…
  datacite Courchene, Dallas 2026 置信度 0.66
• Pre-Registered Multi-Dataset Validation of Three-Way Composition III Privacy-Preserving Architecture for Musculoskeletal-Kinematic Clinical Digital Twins
  v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This reproducibility archive accompan…
  datacite Ferlic, Randolph James, Ferlic, Kimberly Kate 2026 置信度 0.66 pre-registrationfederated learningdifferential privacymembership inferencemusculoskeletal kinematics
• On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing
  Federated learning (FL) enables collaborative training of deep learning models across decentralized image archives without requiring data centralization. This paradigm is particularly relevant in remote sensing (RS), where legal regulations, privacy concerns, …
  datacite Lösche, Simon, Büyüktaş, Barış, Adler, Mathis, Zavras, Angelos 等 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciences
• Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection
  Attention layers are the backbone of today's most powerful and impactful models. Models with multi-million and billion parameters rely on contextual knowledge provided by attention layers. However, their use goes well beyond just being the core component of la…
  datacite Ilić, Mihailo, Savić, Miloš, Kurbalija, Vladimir, Ivanović, Mirjana 等 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning
  Parameter-efficient fine-tuning (PEFT), such as low-rank adaptation (LoRA), has recently been adopted in federated learning to reduce communication and computation costs. In this setup, users download a pretrained model from the server prior to fine-tuning, an…
  datacite Sami, Hasin Us, Sen, Swapneel, Guler, Basak 2026 置信度 0.66 Artificial Intelligence (cs.AI)FOS: Computer and information sciences
• FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity
  Although recent robot perception research emphasizes training on data from diverse environments to improve generalization, most existing methods still rely on centralized learning, which is inefficient and difficult to scale across heterogeneous robot platform…
  datacite Lee, Ganghyeon, Lee, Inha, Lee, Junhee, Lee, Jeongeon 等 2026 置信度 0.66 Robotics (cs.RO)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciences
• Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks
  Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this …
  datacite Ghazikor, Masoud, Ni, Zhou, Hashemi, Morteza 2026 置信度 0.66 Information Theory (cs.IT)Machine Learning (cs.LG)FOS: Computer and information sciences
• attogram/found-collabs-with-blender: 0001
  Full Changelog: https://github.com/attogram/found-collabs-with-blender/compare/0000...0001 You can cite all versions by using the DOI 10.5281/zenodo.21780485. This DOI represents all versions, and will always resolve to the latest one. Read more. Skip to conte…
  datacite David 2026 置信度 0.66
• attogram/found-collabs-with-blender: 0001
  Full Changelog: https://github.com/attogram/found-collabs-with-blender/compare/0000...0001 You can cite all versions by using the DOI 10.5281/zenodo.21780485. This DOI represents all versions, and will always resolve to the latest one. Read more. Skip to conte…
  datacite David 2026 置信度 0.66
• Análisis descriptivo de Técnicas de Machine Learning para Detección de Anomalías en Sistemas IoT
  Introducción: El Internet de las Cosas (IoT) ha transformado radicalmente sectores estratégicos globales, generando ecosistemas de dispositivos interconectados que producen volúmenes masivos de datos en tiempo real, pero que simultáneamente presentan vulnerabi…
  datacite Flores-Andino, Víctor Manuel, Pérez Insuasti, Juan José 2026 置信度 0.66 Anomaly detectionInternet of ThingsMachine learningFederated learningExplainable Artificial Intelligence
• Análisis descriptivo de Técnicas de Machine Learning para Detección de Anomalías en Sistemas IoT
  Introducción: El Internet de las Cosas (IoT) ha transformado radicalmente sectores estratégicos globales, generando ecosistemas de dispositivos interconectados que producen volúmenes masivos de datos en tiempo real, pero que simultáneamente presentan vulnerabi…
  datacite Flores-Andino, Víctor Manuel, Pérez Insuasti, Juan José 2026 置信度 0.66 Anomaly detectionInternet of ThingsMachine learningFederated learningExplainable Artificial Intelligence
• Proceedings of the 7th Summer School on Cyber-Physical Systems and Internet-of-Things Including AIoT Academy 2026
  Natalie Simson and Johannes EckerPart 1: A Systematic of Digital Design................................................. 1Part 2: Interface-Based Design Flow................................................. 41Part 3: Handshake-Based Design.....................…
  datacite Stojanovic, Radovan, Škraba, Andrej, Đurković, Jovan 2026 置信度 0.66 Summer SchoolCyber Physical SystemsInternet of ThingsEmbedded SystemsSmart Systems
• Proceedings of the 7th Summer School on Cyber-Physical Systems and Internet-of-Things Including AIoT Academy 2026
  Natalie Simson and Johannes EckerPart 1: A Systematic of Digital Design................................................. 1Part 2: Interface-Based Design Flow................................................. 41Part 3: Handshake-Based Design.....................…
  datacite Stojanovic, Radovan, Škraba, Andrej, Đurković, Jovan 2026 置信度 0.66 Summer SchoolCyber Physical SystemsInternet of ThingsEmbedded SystemsSmart Systems
• Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
  Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privacy leakage from individual model updates. This paper presents a privacy-preserving federated learning framework…
  datacite Rajabi, Pouya, Toorani, Mohsen 2026 置信度 0.66 Cryptography and Security (cs.CR)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciences
• Reproducibility package: Perbandingan Komprehensif Model Kecerdasan Buatan untuk Klasifikasi Kode HS: Sebuah Tinjauan Sistematis tentang Kinerja, Tantangan, dan Penerapan
  The user wants me to clean a LaTeX text by removing all AI writing characteristics. Let me analyze the text carefully and apply the checklist: 1. **Remove all em-dash (---) and en-dash (--)**: Replace with commas, colons, parentheses, or new sentences 2. **Rem…
  datacite Mahfud, Syarif, Awangga, Rolly Maulana 2026 置信度 0.66 systematic literature reviewPRISMAreproducibilityharmonized systemmachine learning
• Reproducibility package: Perbandingan Komprehensif Model Kecerdasan Buatan untuk Klasifikasi Kode HS: Sebuah Tinjauan Sistematis tentang Kinerja, Tantangan, dan Penerapan
  The user wants me to clean a LaTeX text by removing all AI writing characteristics. Let me analyze the text carefully and apply the checklist: 1. **Remove all em-dash (---) and en-dash (--)**: Replace with commas, colons, parentheses, or new sentences 2. **Rem…
  datacite Mahfud, Syarif, Awangga, Rolly Maulana 2026 置信度 0.66 systematic literature reviewPRISMAreproducibilityharmonized systemmachine learning
• Accurately Inferring Missing Factor VIII Concentrate Dosing Information from Health-Record Data in Patients with Haemophilia A
  This poster, presented at the ISTH 2026 Congress (International Society on Thrombosis and Haemostasis), showcases research conducted within the PHEMS project to address missing treatment information in electronic health records (EHRs) for patients with haemoph…
  datacite Janssen, Alexander, mathot, ron, Cnossen, Marjon H. 2026 置信度 0.66 PHEMSHaemophilia ABenchmarkingData Collection/statistics & numerical dataPediatrics
• Accurately Inferring Missing Factor VIII Concentrate Dosing Information from Health-Record Data in Patients with Haemophilia A
  This poster, presented at the ISTH 2026 Congress (International Society on Thrombosis and Haemostasis), showcases research conducted within the PHEMS project to address missing treatment information in electronic health records (EHRs) for patients with haemoph…
  datacite Janssen, Alexander, mathot, ron, Cnossen, Marjon H. 2026 置信度 0.66 PHEMSHaemophilia ABenchmarkingData Collection/statistics & numerical dataPediatrics
• Nautilus: A Verifiable Hierarchical Federated Learning Framework for Vehicular-Edge-Cloud Systems
  Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue…
  datacite Wu, Linyang, Jia, Linpeng, Zhang, Hanwen, Duan, Tiantian 等 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciences
• MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics
  Security and privacy are primordial requirements for Federated Learning (FL), especially in fields such as healthcare and genomics where sensitive information has to be analyzed. Our FL framework is designed to address these challenges while proposing a modula…
  datacite Largillier, Paul, Paygambar, Karl, Gouy-Pailler, Cédric, Meyer, Vincent 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesE.3; C.2.4; I.2.11; J.3
• Poison to Detect: Detection of Targeted Overfitting in Federated Learning
  Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET). Despite its privacy benefits, FL remains vulnerable to orchestrator-driven privacy attacks. In …
  datacite Mestari, Soumia Zohra El, Zuziak, Maciej Krzysztof, Lenzini, Gabriele 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesK.4.1; I.2.6; K.6.5
• FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities
  Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective collaboration across clients. Existing methods typically rely on generative imput…
  datacite Liang, Haochen, Zhang, Jie, Ochiai, Hideya 2026 置信度 0.66 Multimedia (cs.MM)Machine Learning (cs.LG)FOS: Computer and information sciences
• Consultation Response: Making public services work for you with your digital identity
  Consultation response submitted to the Cabinet Office consultation on Making Public Services Work for You with Your Digital Identity (April 2026). This submission argues that the failure modes of a national digital ID are more likely to be governance failures …
  datacite Shabad, Vsevolod 2026 置信度 0.66 digital identitynational digital IDcybersecurity governancejurisdictional exposurezero trust architecture
• Consultation Response: Making public services work for you with your digital identity
  Consultation response submitted to the Cabinet Office consultation on Making Public Services Work for You with Your Digital Identity (April 2026). This submission argues that the failure modes of a national digital ID are more likely to be governance failures …
  datacite Shabad, Vsevolod 2026 置信度 0.66 digital identitynational digital IDcybersecurity governancejurisdictional exposurezero trust architecture